The Critical Role of API Security in the Internet of Things (IoT)

From smart homes to wearable devices to connected cars, the Internet of Things (IoT) is bringing about a new era of hyper-connectivity. Experts expect investments in the IoT ecosystem to rise above $1 trillion in 2026 — with no signs of slowing down. Application programming interfaces (APIs) are the backbone of IoT, ensuring scalability and security across billions of connected devices.

But because any internet-connected device is an entry point to a larger network, the sheer scale of the IoT comes with cybersecurity risks. API security is necessary to properly secure IoT devices and protect data during the transmission process, allowing access only to authorized devices and detecting potential threats to the API.

This guide will explore how to build a comprehensive IoT API security strategy to protect devices in IoT deployments, addressing both current challenges and emerging trends in the field.

Understanding the role of APIs in IoT

The fundamentals of APIs in IoT ecosystems

Application programming interfaces (APIs) are a set of protocols, tools, and definitions used to build and integrate application software. They allow different applications to communicate with each other through data sharing, without transferring foundational code or architecture.

APIs can be public or private depending on whether they need to be accessible to developers outside an organization or limited internally to connect different systems, software, etc. These functionalities encourage data management and analysis, making it easier for developers to build new software applications. 

In IoT deployment, a remote API connects to the IoT device. The API then transfers data from the connected device to an application, platform, or server to make a request. Conversely, APIs can also instruct the connected device to carry out an action or a set of actions.

The critical functions of APIs in IoT

APIs are the foundational tools that enable software to remotely access IoT devices around the world — but how does the communication between IoT devices and servers actually work?

• Promoting discoverability: APIs support seamless device detection and onboarding to bring IoT devices onto networks. For example, devices can send out discoverability data that APIs immediately recognize.
• Providing standard architecture: APIs provide a routine set of protocols and tools that IoT devices and servers use to exchange data without the need for underlying code. This process improves both the effectiveness and reliability of the interaction.
• Facilitating data transmission: Through APIs, IoT devices send data to the appropriate cloud server or platform to ingest, analyze, and store it instantly.
• Allowing control signals: APIs let applications and servers send signals to IoT devices so that they execute a set of actions, such as answering a user's question or turning on their car.
• Enabling interoperability: APIs play a crucial role in ensuring different IoT devices and systems can work together seamlessly, regardless of their manufacturers or underlying technologies.
• Supporting edge computing: With the rise of edge computing in IoT, APIs are increasingly important for managing data processing at the edge, reducing latency and improving real-time responsiveness.

Unique API security challenges in IoT

Every IoT device is vulnerable to cyberattacks, meaning that lazy or ineffective API security measures may quickly expand the attack surface. Attackers can mine sensitive data, such as customer financial details, contact information, or company trade secrets, from these IoT devices — creating a unique set of circumstances for API security.

The distribution of IoT introduces several security complexities, especially as it relates to scalability. APIs must be capable of securing billions of devices across different types of infrastructure while accommodating resource-limited IoT endpoints.

Additionally, APIs must provide end-to-end data protection for sensitive data traveling from the device to the server and back — which can be precarious with multiple layers of security concerns related to devices, network, and cloud connectivity.

Lastly, time-sensitive IoT applications, such as autonomous vehicles or wearable medical devices, require instant and accurate data responses that can distract from overall security. 

Join us at Kong API Summit to as Katie Paxton-Fear from Traceable.ai talks about API vulnerabilities and why hackers love them so much, including some of the best bug bounty finds and how they were discovered (and anyone can find them).

IoT scalability

To successfully deploy IoT, companies need to be able to scale their cloud and infrastructure up and down with the data provided by their collection of devices. IoT comprises both software and hardware, so building a connected user experience can get tricky. An IoT deployment needs multiple layers of scalability that can accommodate varying networks, designs, and requirements. 

Scalability is crucial for providers operating in IoT because, without powerful connectivity, device management, and web development, there are elevated risks of project failure.

With more connected devices comes more attack surfaces; bots can locate vulnerabilities in devices and target entire servers through DDoS attacks, making a solid API security strategy imperative for deploying IoT products at scale.

Addressing scalability challenges

1. Microservices architecture: Adopting a microservices architecture can help in scaling API services independently, allowing for better resource allocation and management.
2. Auto-scaling solutions: Implement auto-scaling solutions that can dynamically adjust resources based on traffic and demand, ensuring optimal performance during peak times.
3. Load balancing: Utilize advanced load balancing techniques to distribute API requests across multiple servers, improving both performance and reliability.
4. Caching strategies: Implement efficient caching strategies to reduce the load on backend systems and improve response times for frequently accessed data.

Device diversity 

IoT ecosystems are massive, with a broad diversity of devices and formats that can make it difficult to integrate, analyze, and secure the data. When an IoT provider doesn’t implement tight security standards and requirements for all device types, it can create room for error.

Managing these IoT devices is yet another challenging obstacle because servers need to quickly load new features and security updates onto different hardware and software designs when they discover vulnerabilities. If companies cannot integrate varied hardware infrastructure with cloud software and connectivity providers, they may not be able to protect all connected devices in a timely manner.

Strategies for managing device diversity

1. Standardized protocols: Adopt standardized communication protocols (e.g., MQTT, CoAP) to ensure compatibility across diverse devices.
2. Device fingerprinting: Implement device fingerprinting techniques to identify and categorize different types of IoT devices, allowing for tailored security measures.
3. Adaptive security policies: Develop adaptive security policies that can adjust based on the specific characteristics and capabilities of each device type.
4. Unified device management platforms: Utilize unified device management platforms that can handle a wide range of device types, simplifying the process of updating and securing diverse IoT ecosystems.

Real-time IoT requirements

Some IoT devices have real-time requirements that need to be completed by APIs within a specified period. If data returned for real-time requirement requests is inaccurate, there can be potentially catastrophic consequences. IoT providers may compromise security to improve API performance for these requests, but such operational errors can bring down the entire system.

APIs must keep each real-time requirement separate, or they can interfere with one another and compromise the whole network if one fails.

In the IoT environment, APIs often connect with automated equipment, monitoring systems, and other APIs to complete real-time requirements, meaning that all of these communications need airtight security to prevent code injection attacks, priority inversion bugs, and other threats.

Balancing real-time performance and security

1. Edge computing: Leverage edge computing to process time-sensitive data closer to the source, reducing latency and improving real-time responsiveness.
2. Prioritization mechanisms: Implement prioritization mechanisms that can distinguish between time-critical and non-critical API requests, ensuring that real-time requirements are met without compromising overall system security.
3. Asynchronous processing: Utilize asynchronous processing techniques for non-time-critical tasks to balance the load and maintain system responsiveness.
4. Performance monitoring: Implement robust performance monitoring tools to quickly identify and address any bottlenecks or issues affecting real-time performance.

Essential API security measures for IoT

It's important to consider API design, as poorly designed APIs make your IoT data an attractive target for attackers.

One of the best tools to add to your IoT API security arsenal is an API gateway, which acts as a single entry point for interactions between IoT devices and cloud-based services. API gateways provide a standardized way to access and control devices and securely manage data flows, making them an invaluable asset to IoT providers.

Let’s take a closer look at a few of the major IoT security measures that development teams should consider implementing.

Want to learn more about the importance of strategy in your approach to API security? Check out the Becoming a Secure API-First Company eBook.

API authentication

IoT devices require a strong authentication infrastructure — though many currently have little to no authentication protocols. Passwords aren't enough because they can be stored in an application’s source code, making them publicly available. Attackers can access an entire server through stolen passwords or use a bot to administer malware or DDoS attacks.

IoT providers should make their ecosystem more secure by requiring multiple authentication steps, using API keys, and creating a stronger default password baseline to help control the data that IoT devices transmit to their APIs.

Advanced authentication strategies for IoT

1. Multi-factor authentication (MFA): Implement MFA for critical IoT devices and administrative access points.
2. OAuth 2.0 and OpenID Connect: Utilize OAuth 2.0 in combination with OpenID Connect for robust authentication and authorization.
3. Device certificates: Implement device certificates for secure device authentication, especially for industrial IoT applications.
4. Biometric authentication: For consumer IoT devices, consider integrating biometric authentication methods where applicable.
5. JSON web tokens (JWTs): Use JWTs for secure transmission of authentication information between IoT devices and servers.

Data encryption

Another non-negotiable step to ensure IoT API security is data encryption, because it helps prevent attackers from accessing, manipulating, or stealing sensitive information. Data encryption creates undecipherable data with SSL (Secure Sockets Layer) or TLS (Transport Layer Security) protocols, protecting data in transit and at rest.

Developers should consider using an API gateway in conjunction with encryption and access controls to better protect both IoT devices and cloud servers from external threats.

Encryption techniques for IoT

1. End-to-end encryption: Implement end-to-end encryption for sensitive data, ensuring that information remains encrypted throughout its entire journey.
2. Quantum-resistant encryption: Begin exploring and implementing quantum-resistant encryption algorithms to future-proof IoT security.
3. Homomorphic encryption: Consider homomorphic encryption for scenarios where data needs to be processed while remaining encrypted, particularly useful for edge computing in IoT.
4. Attribute-based encryption (ABE): Implement ABE for fine-grained access control in IoT environments with diverse user roles and device types.

Secure coding 

Of course, implementing a secure set of coding practices is an advisable strategy for development teams, too. Securely coded gateways reduce the attack surface and blast radius by limiting lateral mobility in the event of an attack. Similarly, they minimize vulnerabilities that could lead to injection attacks or access control issues.

A quality API gateway has bulletproof infrastructure, up-to-date software, and a solid security network.

Best practices for secure coding in IoT

1. Input validation: Implement thorough input validation to prevent injection attacks and other vulnerabilities.
2. Error handling: Ensure proper error handling to avoid exposing sensitive information through error messages.
3. Secure communications: Use secure communication protocols (e.g., MQTT over TLS) for all device-to-cloud and device-to-device communications.
4. Regular code audits: Conduct regular code audits and vulnerability assessments to identify and address potential security issues.
5. Secure boot: Implement secure boot mechanisms to ensure the integrity of IoT device firmware.

Securing IoT data with APIs

To secure IoT data in transit, APIs mandate encryption protocols for all data that moves between IoT devices and servers.

Additionally, APIs implement access controls in their interfaces, selectively restricting access to authorized applications or users. This strategy works alongside robust authentication techniques, such as API tokens or keys, which ensure safe access and prevent man-in-the-middle attacks. 

For data at rest, APIs require databases to use encryption for data store protection. By pairing encryption with secure coding practices like input validation and key rotation, developers can prevent data at rest from being compromised. APIs can also anonymize restful IoT data fields to provide privacy while maintaining access logs to monitor behavior patterns for early threat detection.

There are data privacy and compliance concerns to keep in mind when using IoT. IoT systems and providers may be subject to regulations like GDPR or CCPA when collecting data from devices. Providers must also comply with data protection, privacy, and security rules that may require them to obtain consent when collecting, processing, and storing personal data through IoT devices.

The more transparency with users, the better — which is why providers should establish clear data handling protocols and privacy policies from the get-go. Any provider that does not comply with compliance regulations or privacy requirements could face legal consequences that derail their IoT deployments entirely.

Advanced data protection strategies

1. Data minimization: Implement data minimization principles, collecting and processing only the necessary data to fulfill specific purposes.
2. Data anonymization and pseudonymization: Utilize advanced techniques for data anonymization and pseudonymization to protect user privacy while maintaining data utility.
3. Federated learning: Explore federated learning approaches for IoT devices to improve data privacy by keeping sensitive data on local devices while still contributing to global models.
4. Blockchain for data integrity: Consider using blockchain technology to ensure the integrity and traceability of critical IoT data.

Examples of API security practices in IoT

API security is involved in every aspect of IoT deployment, including activating and deactivating devices, preventing fraud through alerts, enforcing data usage limits, and more. 

API security strategies protect IoT data from external attackers beyond just encryption. For example, if an API is vulnerable, attackers can engineer it to return a user’s information when they use the IoT device — which can eventually lead to DDoS attacks.

An API-aware web application firewall (WAF) is useful in this case because it can protect IoT devices by filtering and monitoring traffic between the device and the internet. This tactic also helps developers detect malicious threats and respond to them quickly in the event of a data breach. 

Without APIs to connect devices and servers, it would be impossible to efficiently deploy IoT products at scale. With that being said, APIs also come with a specific set of risks that can impact IoT security, and API development teams should test APIs used in IoT deployments for vulnerabilities.

A robust API management solution can help identify and mitigate vulnerabilities in your APIs so you can scale IoT products smoothly.

Emerging API security practices for IoT

1. API sandboxing: Implement API sandboxing techniques to test and validate APIs in isolated environments before deployment.
2. Continuous API monitoring: Utilize advanced monitoring tools that can detect anomalies in API usage patterns and potential security threats in real-time.
3. API versioning and lifecycle management: Implement robust API versioning and lifecycle management practices to ensure secure transitions and deprecations of API versions.
4. GraphQL for efficient data querying: Consider using GraphQL for more efficient and secure data querying in IoT applications, reducing over-fetching and under-fetching of data.

The future of API security in IoT

API security will need to evolve alongside the IoT ecosystem, and there are a few key emerging trends and technologies that will shape its future. 

AI and machine learning

AI and machine learning offer exciting advancements for IoT API security. Machine learning models provide intelligent, real-time threat detection for APIs by performing security analysis from structured and unstructured data sources. These models can identify suspicious IP addresses, abnormalities in API traffic, and unusual server logs to flag suspicious activities or behaviors.

Machine learning will further streamline security analysis to accommodate the time-sensitive nature of IoT deployments, enabling continuous risk assessment, critical decision-making, and improved security insights. 

Emerging AI/ML applications in IoT security

1. Predictive threat detection: Develop AI models that can predict potential security threats based on historical data and current trends.
2. Automated incident response: Implement AI-driven systems that can automatically respond to and mitigate security incidents in real time.
3. Behavioral analysis: Utilize machine learning for advanced behavioral analysis of IoT devices and users to detect anomalies and potential security breaches.

Blockchain

Blockchain is a resilient system that limits data access by default, and it is highly secure — giving it a bright future in API security. Blockchain technology acts as a kind of encrypted data storage that can strengthen API security from the ground up, and its decentralized networks establish trust in digital identities, making it a difficult target for malicious attacks.

Blockchain requires some form of digital identity to post a transaction ledger, but it can mask those identities to help create access control and identity management for APIs involved in IoT deployments. This technique presents the possibility of embedded security for all IoT devices. 

Zero Trust architecture

The Zero Trust API security model is gaining traction because it operates on the belief that organizations should not automatically trust any entity attempting to access a network.

Traditional security models assume that all entities within an organization are trustworthy, but Zero Trust always verifies these entities before granting them access to data or networks. This model is increasingly relevant for IoT API security because APIs often handle sensitive data and IoT connectivity — meaning that every API request must be authenticated, authorized, and validated for proactive protection.

Conclusion 

There’s no question that organizations need a bulletproof API security strategy to protect APIs powering the IoT ecosystem. Devices that are connected to the internet will always carry some level of risk, but strong authentication, encryption, and coding techniques can prevent malicious attacks.

The best way to strengthen your API security strategy is to use an API gateway solution that identifies threats and proactively squashes them. Kong’s cloud-native API platform offers robust authentication mechanisms and secure data transmission for built-in, end-to-end security — so you can scale more and worry less.

Ready to learn more? Request a demo today.